@article{Liao2025,

title = {Joint Temporal and Spectral Processing for Improved Digital Subtraction Angiography using Photon-Counting Detectors},

author = {Suyu Liao and Xiaoxuan Zhang and Xiao Jiang and Matt Tivnan and J. Webster Stayman and Grace Gang},

url = {https://ieeexplore.ieee.org/document/11006116

https://pmc.ncbi.nlm.nih.gov/articles/PMC12703583/},

doi = {10.1109/TBME.2025.3570925},

year  = {2025},

date = {2025-12-01},

journal = {IEEE Trans. Biomedical Engineering},

volume = {72},

number = {12},

pages = {3501-3514},

keywords = {MBIR, Photon Counting, Spectral X-ray/CT},

pubstate = {published},

tppubtype = {article}

}

@article{Jiang2024d,

title = {Multi-Material Decomposition Using Spectral Diffusion Posterior Sampling },

author = {Xiao Jiang and Grace Gang and J. Webster Stayman},

url = {https://ieeexplore.ieee.org/abstract/document/10892198},

doi = {10.1109/TBME.2025.3543747},

year  = {2025},

date = {2025-08-01},

urldate = {2024-08-02},

journal = {IEEE Transactions on Biomedical Engineering},

volume = {72},

number = {8},

pages = {2447-2461},

keywords = {Machine Learning/Deep Learning, MBIR},

pubstate = {published},

tppubtype = {article}

}

@article{Li2023,

title = {Diffusion Posterior Sampling for Nonlinear CT Reconstruction},

author = {Shudong Li and Xiao Jiang and Matt Tivnan and Grace Gang and Yuan Shen and J. Webster Stayman},

url = {https://www.spiedigitallibrary.org/journals/journal-of-medical-imaging/volume-11/issue-4/043504/CT-reconstruction-using-diffusion-posterior-sampling-conditioned-on-a-nonlinear/10.1117/1.JMI.11.4.043504.short},

doi = {10.1117/1.JMI.11.4.043504},

year  = {2024},

date = {2024-08-30},

urldate = {2024-08-30},

journal = {Journal of Medical Imaging},

volume = {11},

issue = {4},

pages = {043504 },

note = {** Featured on Cover **},

keywords = {-Awards-, High-Fidelity Modeling, Machine Learning/Deep Learning, MBIR},

pubstate = {published},

tppubtype = {article}

}

@article{Jiang2024c,

title = {Strategies for CT Reconstruction using Diffusion Posterior Sampling with a Nonlinear Model },

author = {Xiao Jiang and Shudong Li and Peiqing Teng and Grace Gang and J. Webster Stayman },

url = {https://arxiv.org/abs/2407.12956},

year  = {2024},

date = {2024-07-17},

urldate = {2024-07-17},

journal = {TBD},

keywords = {Fast Algorithms, Machine Learning/Deep Learning, MBIR},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{Liu2022,

title = {Model-based three-material decomposition in dual-energy CT using the volume conservation constraint},

author = {Stephen Liu and Matt Tivnan and Greg M. Osgood and Jeffrey H. Siewerdsen and J. Webster Stayman and Wojciech Zbijewski},

url = {https://iopscience.iop.org/article/10.1088/1361-6560/ac7a8b/meta

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9297826/},

doi = {10.1088/1361-6560/ac7a8b},

year  = {2022},

date = {2022-07-08},

journal = {Physics in Medicine and Biology},

volume = {67},

issue = {14},

pages = {145006},

keywords = {MBIR, Spectral X-ray/CT},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Design Optimization of Spatial-Spectral Filters for Cone-Beam CT Material Decomposition},

author = {Matt Tivnan and Wenying Wang and Grace Gang and J. Webster Stayman },

url = {https://pubmed.ncbi.nlm.nih.gov/35377842/, https://ieeexplore.ieee.org/abstract/document/9748122},

doi = {10.1109/TMI.2022.3164568},

year  = {2022},

date = {2022-04-04},

journal = {IEEE Transactions on Medical Imaging},

volume = {41},

issue = {9},

pages = {2399-2413},

keywords = {MBIR, Spectral X-ray/CT, System Design},

pubstate = {published},

tppubtype = {article}

}

@article{Wang2021b,

title = {High-resolution Model-based Material Decomposition in Dual-layer Flat-panel CBCT},

author = {Wenying Wang and Yiqun Ma and Matt Tivnan and Junyuan Li and Grace Gang and Wojciech Zbijewski and Minghui Lu and Jin Zhang and Josh Star-Lack and Richard Colbeth and J. Webster Stayman },

url = {https://pubmed.ncbi.nlm.nih.gov/34272890/, https://aapm.onlinelibrary.wiley.com/doi/full/10.1002/mp.14894},

doi = { 10.1002/mp.14894 },

year  = {2021},

date = {2021-10-01},

journal = {Medical Physics},

volume = {48},

issue = {10},

pages = {6375-6387},

keywords = {High-Fidelity Modeling, High-Resolution CT, MBIR, Spectral X-ray/CT},

pubstate = {published},

tppubtype = {article}

}

@article{Flores2021,

title = {Direct reconstruction of anatomical change in low-dose lung nodule surveillance},

author = {Jessica Flores and Grace Gang and Hao Zhang and Cheng Ting Lin and Shui K Fung and J. Webster Stayman},

url = {https://pubmed.ncbi.nlm.nih.gov/33846692/},

doi = {10.1117/1.JMI.8.2.023503 },

year  = {2021},

date = {2021-04-01},

urldate = {2021-04-01},

journal = {Journal of Medical Imaging},

volume = {8},

number = {2},

pages = {023503},

keywords = {Image Registration, Lungs, MBIR, Prior Images},

pubstate = {published},

tppubtype = {article}

}

@article{Sisniega2021b,

title = {Accelerated 3D image reconstruction with a morphological pyramid and noise-power convergence criterion },

author = {Alejandro Sisniega and J. Webster Stayman and Sarah Capostagno and Clifford Weiss and Tina Ehtiati and Jeffrey H. Siewerdsen},

url = {https://pubmed.ncbi.nlm.nih.gov/33477131/},

doi = {10.1088/1361-6560/abde97 },

year  = {2021},

date = {2021-02-20},

journal = {Physics in Medicine and Biology},

volume = {66},

number = {5},

pages = {055012},

keywords = {Analysis, Fast Algorithms, MBIR},

pubstate = {published},

tppubtype = {article}

}

@article{Liu2020bb,

title = {Model-based dual-energy tomographic image reconstruction of objects containing known metal components},

author = {Stephen Liu and Qian Cao and Matt Tivnan and Steven Tilley and Jeffrey H. Siewerdsen and J. Webster Stayman and Wojciech Zbijewski},

url = {https://pubmed.ncbi.nlm.nih.gov/33113519/},

doi = {10.1088/1361-6560/abc5a9},

year  = {2020},

date = {2020-12-15},

journal = {Physics in Medicine and Biology},

volume = {65},

number = {24},

pages = {245046},

keywords = {Artifact Correction, Known Components, MBIR, Metal Artifacts, Spectral X-ray/CT},

pubstate = {published},

tppubtype = {article}

}

@article{Tivnan2020bb,

title = {A Preconditioned Algorithm for Model-Based Iterative CT Reconstruction and Material Decomposition from Spectral CT Data},

author = {Matt Tivnan and Wenying Wang and J. Webster Stayman},

url = {https://arxiv.org/abs/2010.01371},

year  = {2020},

date = {2020-10-03},

journal = {arXiv preprint },

volume = {arXiv:2010.01371},

keywords = {MBIR, Spectral X-ray/CT},

pubstate = {published},

tppubtype = {article}

}

@article{Wu2020,

title = {Cone-beam CT for imaging of the head/brain: Development and assessment of scanner prototype and reconstruction algorithms },

author = {Pengwei Wu and Alejandro Sisniega and J. Webster Stayman and Wojciech Zbijewski and David H. Foos and Xiaohui Wang and Nishanth Khanna and Nafi Aygun and R. Stevens and Jeffrey H. Siewerdsen},

url = {https://pubmed.ncbi.nlm.nih.gov/32145076/},

doi = {10.1002/mp.14124},

year  = {2020},

date = {2020-06-01},

journal = {Medical Physics},

volume = {47},

number = {6},

pages = {2392-2407},

keywords = {CBCT, Head/Neck, MBIR, System Assessment, System Design},

pubstate = {published},

tppubtype = {article}

}

@article{Hehn2019,

title = {Blind deconvolution in model-based iterative reconstruction for CT using a normalized sparsity measure},

author = {Lorenz Hehn and Steven Tilley and Franz Pfeiffer and J. Webster Stayman},

url = {https://pubmed.ncbi.nlm.nih.gov/31561247/},

doi = {10.1088/1361-6560/ab489e},

year  = {2019},

date = {2019-10-01},

journal = {Physics in Medicine and Biology},

volume = {64},

number = {21},

pages = {215010},

keywords = {High-Resolution CT, MBIR, Regularization Design},

pubstate = {published},

tppubtype = {article}

}

@article{Zhang2019b,

title = {Known-component 3D image reconstruction for improved intraoperative imaging in spine surgery: A clinical pilot study},

author = {Xiaoxuan Zhang and Ali Uneri and J. Webster Stayman and C. C. Zygourakis and S. L. Lo and Nick Theodore and Jeffrey H. Siewerdsen},

url = {https://pubmed.ncbi.nlm.nih.gov/31180586/},

doi = {10.1002/mp.13652},

year  = {2019},

date = {2019-08-01},

journal = {Medical Physics},

volume = {46},

number = {8},

pages = {3483-95},

abstract = {Purpose: Intraoperative imaging plays an increased role in support of surgical guidance and quality assurance for interventional approaches. However, image quality sufficient to detect complications and provide quantitative assessment of the surgical product is often confounded by image noise and artifacts. In this work, we translated a three-dimensional model-based image reconstruction (referred to as "Known-Component Reconstruction," KC-Recon) for the first time to clinical studies with the aim of resolving both limitations.



Methods: KC-Recon builds upon a penalized weighted least-squares (PWLS) method by incorporating models of surgical instrumentation ("known components") within a joint image registration-reconstruction process to improve image quality. Under IRB approval, a clinical pilot study was conducted with 17 spine surgery patients imaged under informed consent using the O-arm cone-beam CT system (Medtronic, Littleton MA) before and after spinal instrumentation. Volumetric images were generated for each patient using KC-Recon in comparison to conventional filtered backprojection (FBP). Imaging performance prior to instrumentation ("preinstrumentation") was evaluated in terms of soft-tissue contrast-to-noise ratio (CNR) and spatial resolution. The quality of images obtained after the instrumentation ("postinstrumentation") was assessed by quantifying the magnitude of metal artifacts (blooming and streaks) arising from pedicle screws. The potential low-dose advantages of the algorithm were tested by simulating low-dose data (down to one-tenth of the dose of standard protocols) from images acquired at normal dose.



Results: Preinstrumentation images (at normal clinical dose and matched resolution) exhibited an average 24.0% increase in soft-tissue CNR with KC-Recon compared to FBP (N = 16, P = 0.02), improving visualization of paraspinal muscles, major vessels, and other soft-tissues about the spine and abdomen. For a total of 72 screws in postinstrumentation images, KC-Recon yielded a significant reduction in metal artifacts: 66.3% reduction in overestimation of screw shaft width due to blooming (P < 0.0001) and reduction in streaks at the screw tip (65.8% increase in attenuation accuracy, P < 0.0001), enabling clearer depiction of the screw within the pedicle and vertebral body for an assessment of breach. Depending on the imaging task, dose reduction up to an order of magnitude appeared feasible while maintaining soft-tissue visibility and metal artifact reduction.



Conclusions: KC-Recon offers a promising means to improve visualization in the presence of surgical instrumentation and reduce patient dose in image-guided procedures. The improved soft-tissue visibility could facilitate the use of cone-beam CT to soft-tissue surgeries, and the ability to precisely quantify and visualize instrument placement could provide a valuable check against complications in the operating room (cf., postoperative CT).



Keywords: cone-beam CT; image-guided procedures; intraoperative imaging; model-based image reconstruction; patient safety.},

keywords = {CBCT, Image Guided Surgery, Known Components, MBIR, Metal Artifacts, Spine},

pubstate = {published},

tppubtype = {article}

}

@article{Stayman2019,

title = {Task-driven source–detector trajectories in cone-beam computed tomography: I. Theory and methods},

author = {J. Webster Stayman and Sarah Capostagno and Grace Gang and Jeffrey H. Siewerdsen },

url = {https://www.spiedigitallibrary.org/journals/Journal-of-Medical-Imaging/volume-6/issue-2/025002/Task-driven-sourcedetector-trajectories-in-cone-beam-computed-tomography/10.1117/1.JMI.6.2.025002.full},

doi = {10.1117/1.JMI.6.2.025002},

year  = {2019},

date = {2019-05-02},

journal = {Journal of Medical Imaging},

volume = {6},

number = {2},

pages = {025002},

keywords = {CBCT, Customized Acquisition, Image Guided Surgery, MBIR, Task-Driven Imaging},

pubstate = {published},

tppubtype = {article}

}

@article{Tilley2019,

title = {Model-based material decomposition with a penalized nonlinear least-squares CT reconstruction algorithm},

author = {Steven Tilley and Wojciech Zbijewski and J. Webster Stayman },

url = {https://pubmed.ncbi.nlm.nih.gov/30561382/},

doi = {10.1088/1361-6560/aaf973},

year  = {2019},

date = {2019-02-01},

journal = {Physics in Medicine and Biology},

volume = {64},

number = {3},

pages = {035005–1-14},

abstract = {Spectral information in CT may be used for material decomposition to produce accurate reconstructions of material density and to separate materials with similar overall attenuation. Traditional methods separate the reconstruction and decomposition steps, often resulting in undesirable trade-offs (e.g. sampling constraints, a simplified spectral model). In this work, we present a model-based material decomposition algorithm which performs the reconstruction and decomposition simultaneously using a multienergy forward model. In a kV-switching simulation study, the presented method is capable of reconstructing iodine at 0.5 mg ml-1 with a contrast-to-noise ratio greater than two, as compared to 3.0 mg ml-1 for image domain decomposition. The presented method also enables novel acquisition methods, which was demonstrated in this work with a combined kV-switching/split-filter acquisition explored in simulation and physical test bench studies. This novel design used four spectral channels to decompose three materials: water, iodine, and gadolinium. In simulation, the presented method accurately reconstructed concentration value estimates with RMSE values of 4.86 mg ml-1 for water, 0.108 mg ml-1 for iodine and 0.170 mg ml-1 for gadolinium. In test-bench data, the RMSE values were 134 mg ml-1, 5.26 mg ml-1 and 1.85 mg ml-1, respectively. These studies demonstrate the ability of model-based material decomposition to produce accurate concentration estimates in challenging spatial/spectral sampling acquisitions.},

keywords = {High-Fidelity Modeling, MBIR, Spectral X-ray/CT},

pubstate = {published},

tppubtype = {article}

}

@article{Wang2019,

title = {Predicting image properties in penalized‐likelihood reconstructions of flat‐panel CBCT},

author = {Wenying Wang and Grace Gang and Jeffrey H. Siewerdsen and J. Webster Stayman},

url = {https://aapm.onlinelibrary.wiley.com/doi/full/10.1002/mp.13249},

doi = {10.1002/mp.13249},

year  = {2019},

date = {2019-01-01},

journal = {Medical Physics},

volume = {46},

number = {1},

pages = {65-80},

keywords = {Analysis, CBCT, High-Fidelity Modeling, MBIR, Regularization Design},

pubstate = {published},

tppubtype = {article}

}

@article{Wu2018c,

title = {Statistical weights for model-based reconstruction in cone-beam CT with electronic noise and dual-gain detector readout},

author = {Pengwei Wu and J. Webster Stayman and Alejandro Sisniega and Wojciech Zbijewski and David H. Foos and Xiaohui Wang and Nafi Aygun and R. Stevens and Jeffrey H. Siewerdsen},

url = {https://iopscience.iop.org/article/10.1088/1361-6560/aaf0b4/meta},

doi = {10.1088/1361-6560/aaf0b4},

year  = {2018},

date = {2018-12-14},

journal = {Physics in Medicine and Biology},

volume = {63},

number = {24},

pages = {245018},

keywords = {CBCT, High-Fidelity Modeling, MBIR},

pubstate = {published},

tppubtype = {article}

}

@article{Uneri2018c,

title = {Image quality and dose characteristics for an O‐arm intraoperative imaging system with model‐based image reconstruction},

author = {Ali Uneri and Xiaoxuan Zhang and T. Yi and J. Webster Stayman and Patrick Helm and Nick Theodore and Jeffrey H. Siewerdsen},

url = {https://aapm.onlinelibrary.wiley.com/doi/full/10.1002/mp.13167},

doi = {10.1002/mp.13167},

year  = {2018},

date = {2018-09-04},

journal = {Medical Physics},

volume = {45},

number = {11},

pages = {4857-4868},

keywords = {CBCT, MBIR, Spine, System Assessment},

pubstate = {published},

tppubtype = {article}

}

@article{Xu2017,

title = {Polyenergetic known-component CT reconstruction with unknown material compositions and unknown x-ray spectra},

author = {Shiyu Xu and A. Jay Khanna and Jeffrey H. Siewerdsen and J. Webster Stayman },

url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5728157/

http://iopscience.iop.org/article/10.1088/1361-6560/aa6285/meta

},

doi = {10.1088/1361-6560/aa6285},

year  = {2017},

date = {2017-03-28},

journal = {Physics in medicine and biology},

volume = {62},

number = {8},

pages = {3352-3374},

keywords = {Artifact Correction, Beam Hardening, Known Components, MBIR, Metal Artifacts},

pubstate = {published},

tppubtype = {article}

}

@article{pourmorteza2016reconstruction,

title = {Reconstruction of difference in sequential CT studies using penalized likelihood estimation.},

author = {Amir Pourmorteza and Hao Dang and Jeffrey H. Siewerdsen and J. Webster Stayman },

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4948746},

doi = {10.1088/0031-9155/61/5/1986},

issn = {1361-6560},

year  = {2016},

date = {2016-03-01},

journal = {Physics in medicine and biology},

volume = {61},

number = {5},

pages = {1986--2002},

publisher = {IOP Publishing},

abstract = {Characterization of anatomical change and other differences is important in sequential computed tomography (CT) imaging, where a high-fidelity patient-specific prior image is typically present, but is not used, in the reconstruction of subsequent anatomical states. Here, we introduce a penalized likelihood (PL) method called reconstruction of difference (RoD) to directly reconstruct a difference image volume using both the current projection data and the (unregistered) prior image integrated into the forward model for the measurement data. The algorithm utilizes an alternating minimization to find both the registration and reconstruction estimates. This formulation allows direct control over the image properties of the difference image, permitting regularization strategies that inhibit noise and structural differences due to inconsistencies between the prior image and the current data. Additionally, if the change is known to be local, RoD allows local acquisition and reconstruction, as opposed to traditional model-based approaches that require a full support field of view (or other modifications). We compared the performance of RoD to a standard PL algorithm, in simulation studies and using test-bench cone-beam CT data. The performances of local and global RoD approaches were similar, with local RoD providing a significant computational speedup. In comparison across a range of data with differing fidelity, the local RoD approach consistently showed lower error (with respect to a truth image) than PL in both noisy data and sparsely sampled projection scenarios. In a study of the prior image registration performance of RoD, a clinically reasonable capture ranges were demonstrated. Lastly, the registration algorithm had a broad capture range and the error for reconstruction of CT data was 35% and 20% less than filtered back-projection for RoD and PL, respectively. The RoD has potential for delivering high-quality difference images in a range of sequential clinical scenarios including image-guided surgeries and treatments where accurate and quantitative assessments of anatomical change is desired.},

keywords = {MBIR, Prior Images, Sequential CT, Sparse Sampling},

pubstate = {published},

tppubtype = {article}

}

@article{sisniega2015volumetric,

title = {Volumetric CT with sparse detector arrays (and application to Si-strip photon counters).},

author = {Alejandro Sisniega and Wojciech Zbijewski and J. Webster Stayman and Jennifer Xu and Katsuyuki Taguchi and Erik Fredenberg and Mats Lundqvist and Jeffrey H. Siewerdsen },

url = {http://www.ncbi.nlm.nih.gov/pubmed/26611740},

doi = {10.1088/0031-9155/61/1/90},

issn = {1361-6560},

year  = {2016},

date = {2016-01-01},

journal = {Physics in medicine and biology},

volume = {61},

number = {1},

pages = {90--113},

publisher = {IOP Publishing},

abstract = {Novel x-ray medical imaging sensors, such as photon counting detectors (PCDs) and large area CCD and CMOS cameras can involve irregular and/or sparse sampling of the detector plane. Application of such detectors to CT involves undersampling that is markedly different from the commonly considered case of sparse angular sampling. This work investigates volumetric sampling in CT systems incorporating sparsely sampled detectors with axial and helical scan orbits and evaluates performance of model-based image reconstruction (MBIR) with spatially varying regularization in mitigating artifacts due to sparse detector sampling. Volumetric metrics of sampling density and uniformity were introduced. Penalized-likelihood MBIR with a spatially varying penalty that homogenized resolution by accounting for variations in local sampling density (i.e. detector gaps) was evaluated. The proposed methodology was tested in simulations and on an imaging bench based on a Si-strip PCD (total area 5 cm × 25 cm) consisting of an arrangement of line sensors separated by gaps of up to 2.5 mm. The bench was equipped with translation/rotation stages allowing a variety of scanning trajectories, ranging from a simple axial acquisition to helical scans with variable pitch. Statistical (spherical clutter) and anthropomorphic (hand) phantoms were considered. Image quality was compared to that obtained with a conventional uniform penalty in terms of structural similarity index (SSIM), image uniformity, spatial resolution, contrast, and noise. Scan trajectories with intermediate helical width (~10 mm longitudinal distance per 360° rotation) demonstrated optimal tradeoff between the average sampling density and the homogeneity of sampling throughout the volume. For a scan trajectory with 10.8 mm helical width, the spatially varying penalty resulted in significant visual reduction of sampling artifacts, confirmed by a 10% reduction in minimum SSIM (from 0.88 to 0.8) and a 40% reduction in the dispersion of SSIM in the volume compared to the constant penalty (both penalties applied at optimal regularization strength). Images of the spherical clutter and wrist phantoms confirmed the advantages of the spatially varying penalty, showing a 25% improvement in image uniformity and 1.8 × higher CNR (at matched spatial resolution) compared to the constant penalty. The studies elucidate the relationship between sampling in the detector plane, acquisition orbit, sampling of the reconstructed volume, and the resulting image quality. They also demonstrate the benefit of spatially varying regularization in MBIR for scenarios with irregular sampling patterns. Such findings are important and integral to the incorporation of a sparsely sampled Si-strip PCD in CT imaging.},

keywords = {MBIR, Photon Counting, Sparse Sampling, System Design},

pubstate = {published},

tppubtype = {article}

}

@article{tilley2015model,

title = {Model-based iterative reconstruction for flat-panel cone-beam CT with focal spot blur, detector blur, and correlated noise.},

author = {Steven Tilley and Jeffrey H. Siewerdsen and J. Webster Stayman },

url = {http://www.ncbi.nlm.nih.gov/pubmed/26649783},

doi = {10.1088/0031-9155/61/1/296},

issn = {1361-6560},

year  = {2016},

date = {2016-01-01},

journal = {Physics in medicine and biology},

volume = {61},

number = {1},

pages = {296--319},

publisher = {IOP Publishing},

abstract = {While model-based reconstruction methods have been successfully applied to flat-panel cone-beam CT (FP-CBCT) systems, typical implementations ignore both spatial correlations in the projection data as well as system blurs due to the detector and focal spot in the x-ray source. In this work, we develop a forward model for flat-panel-based systems that includes blur and noise correlation associated with finite focal spot size and an indirect detector (e.g. scintillator). This forward model is used to develop a staged reconstruction framework where projection data are deconvolved and log-transformed, followed by a generalized least-squares reconstruction that utilizes a non-diagonal statistical weighting to account for the correlation that arises from the acquisition and data processing chain. We investigate the performance of this novel reconstruction approach in both simulated data and in CBCT test-bench data. In comparison to traditional filtered backprojection and model-based methods that ignore noise correlation, the proposed approach yields a superior noise-resolution tradeoff. For example, for a system with 0.34 mm FWHM scintillator blur and 0.70 FWHM focal spot blur, using the correlated noise model instead of an uncorrelated noise model increased resolution by 42% (with variance matched at 6.9 × 10(-8) mm(-2)). While this advantage holds across a wide range of systems with differing blur characteristics, the improvements are greatest for systems where source blur is larger than detector blur.},

note = {Errata: [1] The fidelity terms in equations 10 and 12 are missing a multiplication by 0.5. [2] Equation 14 should be mu(x_j) = a + b erf (2 sqrt( log(2) (x_j-d) / FWHM ). [3] In section 3.2 a reference to Figure 10(e) should be 9(f).},

keywords = {CBCT, High-Fidelity Modeling, High-Resolution CT, MBIR},

pubstate = {published},

tppubtype = {article}

}

@article{Dang2015,

title = {Prospective regularization design in prior-image-based reconstruction.},

author = {Hao Dang and Jeffrey H. Siewerdsen and J. Webster Stayman },

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4833649},

doi = {10.1088/0031-9155/60/24/9515},

issn = {1361-6560},

year  = {2015},

date = {2015-12-01},

journal = {Physics in medicine and biology},

volume = {60},

number = {24},

pages = {9515--36},

publisher = {IOP Publishing},

abstract = {Prior-image-based reconstruction (PIBR) methods leveraging patient-specific anatomical information from previous imaging studies and/or sequences have demonstrated dramatic improvements in dose utilization and image quality for low-fidelity data. However, a proper balance of information from the prior images and information from the measurements is required (e.g. through careful tuning of regularization parameters). Inappropriate selection of reconstruction parameters can lead to detrimental effects including false structures and failure to improve image quality. Traditional methods based on heuristics are subject to error and sub-optimal solutions, while exhaustive searches require a large number of computationally intensive image reconstructions. In this work, we propose a novel method that prospectively estimates the optimal amount of prior image information for accurate admission of specific anatomical changes in PIBR without performing full image reconstructions. This method leverages an analytical approximation to the implicitly defined PIBR estimator, and introduces a predictive performance metric leveraging this analytical form and knowledge of a particular presumed anatomical change whose accurate reconstruction is sought. Additionally, since model-based PIBR approaches tend to be space-variant, a spatially varying prior image strength map is proposed to optimally admit changes everywhere in the image (eliminating the need to know change locations a priori). Studies were conducted in both an ellipse phantom and a realistic thorax phantom emulating a lung nodule surveillance scenario. The proposed method demonstrated accurate estimation of the optimal prior image strength while achieving a substantial computational speedup (about a factor of 20) compared to traditional exhaustive search. Moreover, the use of the proposed prior strength map in PIBR demonstrated accurate reconstruction of anatomical changes without foreknowledge of change locations in phantoms where the optimal parameters vary spatially by an order of magnitude or more. In a series of studies designed to explore potential unknowns associated with accurate PIBR, optimal prior image strength was found to vary with attenuation differences associated with anatomical change but exhibited only small variations as a function of the shape and size of the change. The results suggest that, given a target change attenuation, prospective patient-, change-, and data-specific customization of the prior image strength can be performed to ensure reliable reconstruction of specific anatomical changes.},

keywords = {MBIR, Prior Images, Regularization Design, Sparse Sampling},

pubstate = {published},

tppubtype = {article}

}

@article{dang2015statistical,

title = {Statistical reconstruction for cone-beam CT with a post-artifact-correction noise model: application to high-quality head imaging.},

author = {Hao Dang and J. Webster Stayman and Alejandro Sisniega and Jennifer Xu and Wojciech Zbijewski and Xiaohui Wang and David H. Foos and Nafi Aygun and Vassilis Koliatsos and Jeffrey H. Siewerdsen },

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4545529},

doi = {10.1088/0031-9155/60/16/6153},

issn = {1361-6560},

year  = {2015},

date = {2015-08-01},

journal = {Physics in medicine and biology},

volume = {60},

number = {16},

pages = {6153--75},

publisher = {IOP Publishing},

abstract = {Non-contrast CT reliably detects fresh blood in the brain and is the current front-line imaging modality for intracranial hemorrhage such as that occurring in acute traumatic brain injury (contrast ~40-80 HU, size textgreater 1 mm). We are developing flat-panel detector (FPD) cone-beam CT (CBCT) to facilitate such diagnosis in a low-cost, mobile platform suitable for point-of-care deployment. Such a system may offer benefits in the ICU, urgent care/concussion clinic, ambulance, and sports and military theatres. However, current FPD-CBCT systems face significant challenges that confound low-contrast, soft-tissue imaging. Artifact correction can overcome major sources of bias in FPD-CBCT but imparts noise amplification in filtered backprojection (FBP). Model-based reconstruction improves soft-tissue image quality compared to FBP by leveraging a high-fidelity forward model and image regularization. In this work, we develop a novel penalized weighted least-squares (PWLS) image reconstruction method with a noise model that includes accurate modeling of the noise characteristics associated with the two dominant artifact corrections (scatter and beam-hardening) in CBCT and utilizes modified weights to compensate for noise amplification imparted by each correction. Experiments included real data acquired on a FPD-CBCT test-bench and an anthropomorphic head phantom emulating intra-parenchymal hemorrhage. The proposed PWLS method demonstrated superior noise-resolution tradeoffs in comparison to FBP and PWLS with conventional weights (viz. at matched 0.50 mm spatial resolution},

keywords = {Artifact Correction, CBCT, Head/Neck, High-Fidelity Modeling, MBIR},

pubstate = {published},

tppubtype = {article}

}

@article{wang2014nesterov,

title = {Accelerated statistical reconstruction for C-arm cone-beam CT using Nesterov's method.},

author = {Adam S. Wang and J. Webster Stayman and Yoshito Otake and Sebastian Vogt and Gerhard Kleinszig and Jeffrey H. Siewerdsen },

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4425726},

doi = {10.1118/1.4914378},

issn = {0094-2405},

year  = {2015},

date = {2015-05-01},

journal = {Medical physics},

volume = {42},

number = {5},

pages = {2699--708},

abstract = {PURPOSE To accelerate model-based iterative reconstruction (IR) methods for C-arm cone-beam CT (CBCT), thereby combining the benefits of improved image quality and/or reduced radiation dose with reconstruction times on the order of minutes rather than hours. METHODS The ordered-subsets, separable quadratic surrogates (OS-SQS) algorithm for solving the penalized-likelihood (PL) objective was modified to include Nesterov's method, which utilizes "momentum" from image updates of previous iterations to better inform the current iteration and provide significantly faster convergence. Reconstruction performance of an anthropomorphic head phantom was assessed on a benchtop CBCT system, followed by CBCT on a mobile C-arm, which provided typical levels of incomplete data, including lateral truncation. Additionally, a cadaveric torso that presented realistic soft-tissue and bony anatomy was imaged on the C-arm, and different projectors were assessed for reconstruction speed. RESULTS Nesterov's method provided equivalent image quality to OS-SQS while reducing the reconstruction time by an order of magnitude (10.0 ×) by reducing the number of iterations required for convergence. The faster projectors were shown to produce similar levels of convergence as more accurate projectors and reduced the reconstruction time by another 5.3 ×. Despite the slower convergence of IR with truncated C-arm CBCT, comparison of PL reconstruction methods implemented on graphics processing units showed that reconstruction time was reduced from 106 min for the conventional OS-SQS method to as little as 2.0 min with Nesterov's method for a volumetric reconstruction of the head. In body imaging, reconstruction of the larger cadaveric torso was reduced from 159 min down to 3.3 min with Nesterov's method. CONCLUSIONS The acceleration achieved through Nesterov's method combined with ordered subsets reduced IR times down to a few minutes. This improved compatibility with clinical workflow better enables broader adoption of IR in CBCT-guided procedures, with corresponding benefits in overcoming conventional limits of image quality at lower dose.},

keywords = {CBCT, Fast Algorithms, MBIR},

pubstate = {published},

tppubtype = {article}

}

@article{wang2015accelerated,

title = {Accelerated statistical reconstruction for C-arm cone-beam CT using Nesterov's method.},

author = {Adam S. Wang and J. Webster Stayman and Yoshito Otake and Sebastian Vogt and Gerhard Kleinszig and Jeffrey H. Siewerdsen },

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4425726},

doi = {10.1118/1.4914378},

issn = {0094-2405},

year  = {2015},

date = {2015-05-01},

journal = {Medical physics},

volume = {42},

number = {5},

pages = {2699--708},

publisher = {American Association of Physicists in Medicine},

abstract = {PURPOSE To accelerate model-based iterative reconstruction (IR) methods for C-arm cone-beam CT (CBCT), thereby combining the benefits of improved image quality and/or reduced radiation dose with reconstruction times on the order of minutes rather than hours. METHODS The ordered-subsets, separable quadratic surrogates (OS-SQS) algorithm for solving the penalized-likelihood (PL) objective was modified to include Nesterov's method, which utilizes "momentum" from image updates of previous iterations to better inform the current iteration and provide significantly faster convergence. Reconstruction performance of an anthropomorphic head phantom was assessed on a benchtop CBCT system, followed by CBCT on a mobile C-arm, which provided typical levels of incomplete data, including lateral truncation. Additionally, a cadaveric torso that presented realistic soft-tissue and bony anatomy was imaged on the C-arm, and different projectors were assessed for reconstruction speed. RESULTS Nesterov's method provided equivalent image quality to OS-SQS while reducing the reconstruction time by an order of magnitude (10.0 ×) by reducing the number of iterations required for convergence. The faster projectors were shown to produce similar levels of convergence as more accurate projectors and reduced the reconstruction time by another 5.3 ×. Despite the slower convergence of IR with truncated C-arm CBCT, comparison of PL reconstruction methods implemented on graphics processing units showed that reconstruction time was reduced from 106 min for the conventional OS-SQS method to as little as 2.0 min with Nesterov's method for a volumetric reconstruction of the head. In body imaging, reconstruction of the larger cadaveric torso was reduced from 159 min down to 3.3 min with Nesterov's method. CONCLUSIONS The acceleration achieved through Nesterov's method combined with ordered subsets reduced IR times down to a few minutes. This improved compatibility with clinical workflow better enables broader adoption of IR in CBCT-guided procedures, with corresponding benefits in overcoming conventional limits of image quality at lower dose.},

keywords = {CBCT, Fast Algorithms, MBIR},

pubstate = {published},

tppubtype = {article}

}

@article{gang2015taskb,

title = {Task-driven image acquisition and reconstruction in cone-beam CT.},

author = {Grace Gang and J. Webster Stayman and Tina Ehtiati and Jeffrey H. Siewerdsen },

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4539970},

doi = {10.1088/0031-9155/60/8/3129},

issn = {1361-6560},

year  = {2015},

date = {2015-04-01},

journal = {Physics in medicine and biology},

volume = {60},

number = {8},

pages = {3129--50},

publisher = {IOP Publishing},

abstract = {This work introduces a task-driven imaging framework that incorporates a mathematical definition of the imaging task, a model of the imaging system, and a patient-specific anatomical model to prospectively design image acquisition and reconstruction techniques to optimize task performance. The framework is applied to joint optimization of tube current modulation, view-dependent reconstruction kernel, and orbital tilt in cone-beam CT. The system model considers a cone-beam CT system incorporating a flat-panel detector and 3D filtered backprojection and accurately describes the spatially varying noise and resolution over a wide range of imaging parameters in the presence of a realistic anatomical model. Task-based detectability index (d') is incorporated as the objective function in a task-driven optimization of image acquisition and reconstruction techniques. The orbital tilt was optimized through an exhaustive search across tilt angles ranging ± 30°. For each tilt angle, the view-dependent tube current and reconstruction kernel (i.e. the modulation profiles) that maximized detectability were identified via an alternating optimization. The task-driven approach was compared with conventional unmodulated and automatic exposure control (AEC) strategies for a variety of imaging tasks and anthropomorphic phantoms. The task-driven strategy outperformed the unmodulated and AEC cases for all tasks. For example, d' for a sphere detection task in a head phantom was improved by 30% compared to the unmodulated case by using smoother kernels for noisy views and distributing mAs across less noisy views (at fixed total mAs) in a manner that was beneficial to task performance. Similarly for detection of a line-pair pattern, the task-driven approach increased d' by 80% compared to no modulation by means of view-dependent mA and kernel selection that yields modulation transfer function and noise-power spectrum optimal to the task. Optimization of orbital tilt identified the tilt angle that reduced quantum noise in the region of the stimulus by avoiding highly attenuating anatomical structures. The task-driven imaging framework offers a potentially valuable paradigm for prospective definition of acquisition and reconstruction protocols that improve task performance without increase in dose.},

keywords = {CBCT, Customized Acquisition, MBIR, Regularization Design, Task-Driven Imaging},

pubstate = {published},

tppubtype = {article}

}

@article{Stayman2015,

title = {Task-Based Optimization of Source-Detector Orbits in Interventional Cone-beam CT},

author = {J. Webster Stayman and Grace Gang and Jeffrey H. Siewerdsen },

url = {https://aiai.jhu.edu/papers/Fully3D2015_stayman.pdf},

year  = {2015},

date = {2015-01-01},

journal = {International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine},

volume = {13},

keywords = {CBCT, Customized Acquisition, MBIR, Task-Driven Imaging},

pubstate = {published},

tppubtype = {article}

}

@article{dang2014dpirple,

title = {dPIRPLE: a joint estimation framework for deformable registration and penalized-likelihood CT image reconstruction using prior images.},

author = {Hao Dang and Adam S. Wang and Marc S. Sussman and Jeffrey H. Siewerdsen and J. Webster Stayman },

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4142353},

doi = {10.1088/0031-9155/59/17/4799},

issn = {1361-6560},

year  = {2014},

date = {2014-09-01},

journal = {Physics in medicine and biology},

volume = {59},

number = {17},

pages = {4799--826},

publisher = {IOP Publishing},

abstract = {Sequential imaging studies are conducted in many clinical scenarios. Prior images from previous studies contain a great deal of patient-specific anatomical information and can be used in conjunction with subsequent imaging acquisitions to maintain image quality while enabling radiation dose reduction (e.g., through sparse angular sampling, reduction in fluence, etc). However, patient motion between images in such sequences results in misregistration between the prior image and current anatomy. Existing prior-image-based approaches often include only a simple rigid registration step that can be insufficient for capturing complex anatomical motion, introducing detrimental effects in subsequent image reconstruction. In this work, we propose a joint framework that estimates the 3D deformation between an unregistered prior image and the current anatomy (based on a subsequent data acquisition) and reconstructs the current anatomical image using a model-based reconstruction approach that includes regularization based on the deformed prior image. This framework is referred to as deformable prior image registration, penalized-likelihood estimation (dPIRPLE). Central to this framework is the inclusion of a 3D B-spline-based free-form-deformation model into the joint registration-reconstruction objective function. The proposed framework is solved using a maximization strategy whereby alternating updates to the registration parameters and image estimates are applied allowing for improvements in both the registration and reconstruction throughout the optimization process. Cadaver experiments were conducted on a cone-beam CT testbench emulating a lung nodule surveillance scenario. Superior reconstruction accuracy and image quality were demonstrated using the dPIRPLE algorithm as compared to more traditional reconstruction methods including filtered backprojection, penalized-likelihood estimation (PLE), prior image penalized-likelihood estimation (PIPLE) without registration, and prior image penalized-likelihood estimation with rigid registration of a prior image (PIRPLE) over a wide range of sampling sparsity and exposure levels.},

keywords = {Image Registration, Lungs, MBIR, Prior Images, Sequential CT, Sparse Sampling},

pubstate = {published},

tppubtype = {article}

}

@article{Gang2014,

title = {Task-based detectability in CT image reconstruction by filtered backprojection and penalized likelihood estimation.},

author = {Grace Gang and J. Webster Stayman and Wojciech Zbijewski and Jeffrey H. Siewerdsen},

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4115652},

doi = {10.1118/1.4883816},

issn = {0094-2405},

year  = {2014},

date = {2014-08-01},

journal = {Medical physics},

volume = {41},

number = {8},

pages = {081902},

publisher = {American Association of Physicists in Medicine},

abstract = {PURPOSE Nonstationarity is an important aspect of imaging performance in CT and cone-beam CT (CBCT), especially for systems employing iterative reconstruction. This work presents a theoretical framework for both filtered-backprojection (FBP) and penalized-likelihood (PL) reconstruction that includes explicit descriptions of nonstationary noise, spatial resolution, and task-based detectability index. Potential utility of the model was demonstrated in the optimal selection of regularization parameters in PL reconstruction. METHODS Analytical models for local modulation transfer function (MTF) and noise-power spectrum (NPS) were investigated for both FBP and PL reconstruction, including explicit dependence on the object and spatial location. For FBP, a cascaded systems analysis framework was adapted to account for nonstationarity by separately calculating fluence and system gains for each ray passing through any given voxel. For PL, the point-spread function and covariance were derived using the implicit function theorem and first-order Taylor expansion according to Fessler ["Mean and variance of implicitly defined biased estimators (such as penalized maximum likelihood): Applications to tomography," IEEE Trans. Image Process. 5(3), 493-506 (1996)]. Detectability index was calculated for a variety of simple tasks. The model for PL was used in selecting the regularization strength parameter to optimize task-based performance, with both a constant and a spatially varying regularization map. RESULTS Theoretical models of FBP and PL were validated in 2D simulated fan-beam data and found to yield accurate predictions of local MTF and NPS as a function of the object and the spatial location. The NPS for both FBP and PL exhibit similar anisotropic nature depending on the pathlength (and therefore, the object and spatial location within the object) traversed by each ray, with the PL NPS experiencing greater smoothing along directions with higher noise. The MTF of FBP is isotropic and independent of location to a first order approximation, whereas the MTF of PL is anisotropic in a manner complementary to the NPS. Task-based detectability demonstrates dependence on the task, object, spatial location, and smoothing parameters. A spatially varying regularization "map" designed from locally optimal regularization can improve overall detectability beyond that achievable with the commonly used constant regularization parameter. CONCLUSIONS Analytical models for task-based FBP and PL reconstruction are predictive of nonstationary noise and resolution characteristics, providing a valuable framework for understanding and optimizing system performance in CT and CBCT.},

keywords = {Analysis, Customized Acquisition, MBIR, Regularization Design, Task-Driven Imaging},

pubstate = {published},

tppubtype = {article}

}

@article{zbijewski2014dual,

title = {Dual-energy cone-beam CT with a flat-panel detector: effect of reconstruction algorithm on material classification.},

author = {Wojciech Zbijewski and Grace Gang and Jennifer Xu and Adam S. Wang and J. Webster Stayman and Katsuyuki Taguchi and John A. Carrino and Jeffrey H. Siewerdsen },

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC3977791},

doi = {10.1118/1.4863598},

issn = {0094-2405},

year  = {2014},

date = {2014-02-01},

journal = {Medical physics},

volume = {41},

number = {2},

pages = {021908},

publisher = {American Association of Physicists in Medicine},

abstract = {PURPOSE Cone-beam CT (CBCT) with a flat-panel detector (FPD) is finding application in areas such as breast and musculoskeletal imaging, where dual-energy (DE) capabilities offer potential benefit. The authors investigate the accuracy of material classification in DE CBCT using filtered backprojection (FBP) and penalized likelihood (PL) reconstruction and optimize contrast-enhanced DE CBCT of the joints as a function of dose, material concentration, and detail size. METHODS Phantoms consisting of a 15 cm diameter water cylinder with solid calcium inserts (50-200 mg/ml, 3-28.4 mm diameter) and solid iodine inserts (2-10 mg/ml, 3-28.4 mm diameter), as well as a cadaveric knee with intra-articular injection of iodine were imaged on a CBCT bench with a Varian 4343 FPD. The low energy (LE) beam was 70 kVp (+0.2 mm Cu), and the high energy (HE) beam was 120 kVp (+0.2 mm Cu, +0.5 mm Ag). Total dose (LE+HE) was varied from 3.1 to 15.6 mGy with equal dose allocation. Image-based DE classification involved a nearest distance classifier in the space of LE versus HE attenuation values. Recognizing the differences in noise between LE and HE beams, the LE and HE data were differentially filtered (in FBP) or regularized (in PL). Both a quadratic (PLQ) and a total-variation penalty (PLTV) were investigated for PL. The performance of DE CBCT material discrimination was quantified in terms of voxelwise specificity, sensitivity, and accuracy. RESULTS Noise in the HE image was primarily responsible for classification errors within the contrast inserts, whereas noise in the LE image mainly influenced classification in the surrounding water. For inserts of diameter 28.4 mm, DE CBCT reconstructions were optimized to maximize the total combined accuracy across the range of calcium and iodine concentrations, yielding values of ∼ 88% for FBP and PLQ, and ∼ 95% for PLTV at 3.1 mGy total dose, increasing to ∼ 95% for FBP and PLQ, and ∼ 98% for PLTV at 15.6 mGy total dose. For a fixed iodine concentration of 5 mg/ml and reconstructions maximizing overall accuracy across the range of insert diameters, the minimum diameter classified with accuracy textgreater80% was ∼ 15 mm for FBP and PLQ and ∼ 10 mm for PLTV, improving to ∼ 7 mm for FBP and PLQ and ∼ 3 mm for PLTV at 15.6 mGy. The results indicate similar performance for FBP and PLQ and showed improved classification accuracy with edge-preserving PLTV. A slight preference for increased smoothing of the HE data was found. DE CBCT discrimination of iodine and bone in the knee was demonstrated with FBP and PLTV at 6.2 mGy total dose. CONCLUSIONS For iodine concentrations textgreater5 mg/ml and detail size ∼ 20 mm, material classification accuracy of textgreater90% was achieved in DE CBCT with both FBP and PL at total doses textless10 mGy. Optimal performance was attained by selection of reconstruction parameters based on the differences in noise between HE and LE data, typically favoring stronger smoothing of the HE data, and by using penalties matched to the imaging task (e.g., edge-preserving PLTV in areas of uniform enhancement).},

keywords = {CBCT, MBIR, Spectral X-ray/CT},

pubstate = {published},

tppubtype = {article}

}

@article{wang2014soft,

title = {Soft-tissue imaging with C-arm cone-beam CT using statistical reconstruction.},

author = {Adam S. Wang and J. Webster Stayman and Yoshito Otake and Gerhard Kleinszig and Sebastian Vogt and Gary L. Gallia and A. Jay Khanna and Jeffrey H. Siewerdsen },

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4046706},

doi = {10.1088/0031-9155/59/4/1005},

issn = {1361-6560},

year  = {2014},

date = {2014-02-01},

journal = {Physics in medicine and biology},

volume = {59},

number = {4},

pages = {1005--26},

publisher = {IOP Publishing},

abstract = {The potential for statistical image reconstruction methods such as penalized-likelihood (PL) to improve C-arm cone-beam CT (CBCT) soft-tissue visualization for intraoperative imaging over conventional filtered backprojection (FBP) is assessed in this work by making a fair comparison in relation to soft-tissue performance. A prototype mobile C-arm was used to scan anthropomorphic head and abdomen phantoms as well as a cadaveric torso at doses substantially lower than typical values in diagnostic CT, and the effects of dose reduction via tube current reduction and sparse sampling were also compared. Matched spatial resolution between PL and FBP was determined by the edge spread function of low-contrast (∼ 40-80 HU) spheres in the phantoms, which were representative of soft-tissue imaging tasks. PL using the non-quadratic Huber penalty was found to substantially reduce noise relative to FBP, especially at lower spatial resolution where PL provides a contrast-to-noise ratio increase up to 1.4-2.2 × over FBP at 50% dose reduction across all objects. Comparison of sampling strategies indicates that soft-tissue imaging benefits from fully sampled acquisitions at dose above ∼ 1.7 mGy and benefits from 50% sparsity at dose below ∼ 1.0 mGy. Therefore, an appropriate sampling strategy along with the improved low-contrast visualization offered by statistical reconstruction demonstrates the potential for extending intraoperative C-arm CBCT to applications in soft-tissue interventions in neurosurgery as well as thoracic and abdominal surgeries by overcoming conventional tradeoffs in noise, spatial resolution, and dose.},

keywords = {CBCT, Head/Neck, MBIR, System Assessment},

pubstate = {published},

tppubtype = {article}

}

@article{Stayman2013b,

title = {PIRPLE: a penalized-likelihood framework for incorporation of prior images in CT reconstruction.},

author = {J. Webster Stayman and Hao Dang and Yifu Ding and Jeffrey H. Siewerdsen },

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC3868341},

doi = {10.1088/0031-9155/58/21/7563},

issn = {1361-6560},

year  = {2013},

date = {2013-11-01},

journal = {Physics in medicine and biology},

volume = {58},

number = {21},

pages = {7563--82},

abstract = {Over the course of diagnosis and treatment, it is common for a number of imaging studies to be acquired. Such imaging sequences can provide substantial patient-specific prior knowledge about the anatomy that can be incorporated into a prior-image-based tomographic reconstruction for improved image quality and better dose utilization. We present a general methodology using a model-based reconstruction approach including formulations of the measurement noise that also integrates prior images. This penalized-likelihood technique adopts a sparsity enforcing penalty that incorporates prior information yet allows for change between the current reconstruction and the prior image. Moreover, since prior images are generally not registered with the current image volume, we present a modified model-based approach that seeks a joint registration of the prior image in addition to the reconstruction of projection data. We demonstrate that the combined prior-image- and model-based technique outperforms methods that ignore the prior data or lack a noise model. Moreover, we demonstrate the importance of registration for prior-image-based reconstruction methods and show that the prior-image-registered penalized-likelihood estimation (PIRPLE) approach can maintain a high level of image quality in the presence of noisy and undersampled projection data.},

keywords = {Image Registration, MBIR, Prior Images, Sequential CT},

pubstate = {published},

tppubtype = {article}

}